OCDQ Radio on Big Data and Data Science

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

This podcast is no longer an active project, meaning not only do I rarely publish a new episode, but its episodes are only available to listen to on this website and no longer distributed on platforms such as Apple Podcasts and Google Podcasts.

I have been enjoying listening to many of the old episodes since I was happy to hear how evergreen they are, meaning their content is still applicable today. This post is part of my Best of OCDQ Radio series, organizing groups of episodes by topic(s).

Podcast Episodes on Big Data and Data Science

OCDQ Radio on Data Governance

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

This podcast is no longer an active project, meaning not only do I rarely publish a new episode, but its episodes are only available to listen to on this website and no longer distributed on platforms such as Apple Podcasts and Google Podcasts.

I have been enjoying listening to many of the old episodes since I was happy to hear how evergreen they are, meaning their content is still applicable today. This post is part of my Best of OCDQ Radio series, organizing groups of episodes by topic(s).

Podcast Episodes on Data Governance

OCDQ Radio on Data Quality

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

This podcast is no longer an active project, meaning not only do I rarely publish a new episode, but its episodes are only available to listen to on this website and no longer distributed on platforms such as Apple Podcasts and Google Podcasts.

I have been enjoying listening to many of the old episodes since I was happy to hear how evergreen they are, meaning their content is still applicable today. This post is part of my Best of OCDQ Radio series, organizing groups of episodes by topic(s).

Podcast Episodes on Data Quality

Why No One Cares about Poor Data Quality

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

Why does no one care about poor data quality? Because you’re probably measuring data quality without connecting it to your organization’s business processes, applications, or other business uses for enterprise data.

During this episode, I discuss how this is accomplished through the implementation of a data governance policy as an executable process comprised of a combination of business rules and data rules that create and track meaningful data quality metrics framed within a relative business context and associated with a data quality threshold (i.e., tolerance for poor data quality). Each business use for enterprise data should be governed by its own policy. Compliance with these data governance policies aligns data quality with business insight, providing the missing link between poor data quality and poor business performance. And it is then—and only then—that anyone cares about poor data quality.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Total Information Risk Management

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, I am joined by special guest Dr. Alexander Borek, the inventor of Total Information Risk Management (TIRM) and the leading expert on how to apply risk management principles to data management.  Dr. Borek is a frequent speaker at international information management conferences and author of many research articles covering a range of topics, including EIM, data quality, crowd sourcing, and IT business value.  In his current role at IBM, Dr. Borek applies data analytics to drive IBM’s worldwide corporate strategy.  Previously, he led a team at the University of Cambridge to develop the TIRM process and test it in a number of different industries.  He holds a PhD in engineering from the University of Cambridge.

This podcast discusses his book Total Information Risk Management: Maximizing the Value of Data and Information Assets, which is now available world-wide and is a must read for all data and information managers who want to understand and measure the implications of low quality data and information assets.  The book provides step by step instructions, along with illustrative examples from studies in many different industries, on how to implement total information risk management, which will help your organization:

  • Learn how to manage data and information for business value.

  • Create powerful and convincing business cases for all your data and information management, data governance, big data, data warehousing, business intelligence, and business analytics initiatives, projects, and programs.

  • Protect your organization from risks that arise through poor data and information assets.

  • Quantify the impact of having poor data and information.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Data Quality Project Management

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Adam Cox and I discuss data quality project management, avoiding data quality becoming an afterthought on data integration and data migration projects, the difference and relationship between data ownership and data stewardship, regulatory requirements for data quality, and the importance of getting buy-in from business stakeholders.

Adam Cox is a data management professional with over ten years of experience working in the public and private sector in the United Kingdom (UK).  He is an experienced project and technical manager working on large-scale projects involving significant data migration and data integration.  Adam Cox is currently working for an established UK financial institution as a Data Quality Consultant, mainly on regulatory reporting projects.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Measuring Data Quality for Ongoing Improvement

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

Listen to Laura Sebastian-Coleman, author of the book Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework, and I discuss bringing together a better understanding of what is represented in data, and how it is represented, with the expectations for use in order to improve the overall quality of data.  Our discussion also includes avoiding two common mistakes made when starting a data quality project, and defining five dimensions of data quality.

Laura Sebastian-Coleman has worked on data quality in large health care data warehouses since 2003.  She has implemented data quality metrics and reporting, launched and facilitated a data quality community, contributed to data consumer training programs, and has led efforts to establish data standards and to manage metadata.  In 2009, she led a group of analysts in developing the original Data Quality Assessment Framework (DQAF), which is the basis for her book.

Laura Sebastian-Coleman has delivered papers at MIT’s Information Quality Conferences and at conferences sponsored by the International Association for Information and Data Quality (IAIDQ) and the Data Governance Organization (DGO).  She holds IQCP (Information Quality Certified Professional) designation from IAIDQ, a Certificate in Information Quality from MIT, a B.A. in English and History from Franklin & Marshall College, and a Ph.D. in English Literature from the University of Rochester.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Too Big to Ignore

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Phil Simon shares his sage advice for getting started with big data, including the importance of having a data-oriented mindset, that ambitious long-term goals should give way to more reasonable and attainable short-term objectives, and always remembering that big data is just another means toward solving business problems.

Phil Simon is a sought-after speaker and the author of five management books, most recently Too Big to Ignore: The Business Case for Big Data.  A recognized technology expert, he consults companies on how to optimize their use of technology.  His contributions have been featured on NBC, CNBC, ABC News, Inc. magazine, BusinessWeek, Huffington Post, Globe and Mail, Fast Company, Forbes, the New York Times, ReadWriteWeb, and many other sites.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Doing Data Governance

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OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, I discuss the practical aspects of doing data governance with John Ladley, the author of the excellent book Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program.  Our discussion includes understanding the difference and relationship between data governance and information management, the importance of establishing principles before creating policies, data stewardship, and three critical success factors for data governance.

John Ladley is a business technology thought leader with 30 years of experience in improving organizations through the successful implementation of information systems.  He is a recognized authority in the use and implementation of business intelligence and enterprise information management (EIM).

John Ladley is the author of Making EIM Work for Business, and frequently writes and speaks on a variety of technology and enterprise information management topics.  His information management experience is balanced between strategic technology planning, project management, and, most important, the practical application of technology to business problems.

Popular OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Open MIKE Podcast — Episode 12

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Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space.  For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

 

Episode 12: Information Development Book

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

 

MIKE2.0 Content Featured in or Related to this Podcast

Information Development Book: openmethodology.org/wiki/Information_Development_Book

Information Development: openmethodology.org/wiki/Information_Development

 

Previous Episodes of the Open MIKE Podcast

Clicking on the link will take you to the episode’s blog post:

Episode 01: Information Management Principles

Episode 02: Information Governance and Distributing Power

Episode 03: Data Quality Improvement and Data Investigation

Episode 04: Metadata Management

Episode 05: Defining Big Data

Episode 06: Getting to Know NoSQL

Episode 07: Guiding Principles for Open Semantic Enterprise

Episode 08: Information Lifecycle Management

Episode 09: Enterprise Data Management Strategy

Episode 10: Information Maturity QuickScan

Episode 11: Information Maturity Model

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE